Data Science and AI Lab (BSDA4001) | Data Science and Applications | IIT Madras

WEEK 1

Week 1: Data Science & Python Stack Refresher Key tools: NumPy, Pandas, Matplotlib, Seaborn, Jupyter, Git

WEEK 2

Week 2: Machine Learning with Scikit-learn Core models: regression, classification, clustering Pipelines, hyperparameter tuning, evaluation metrics

WEEK 3

Week 3: Deep Learning with PyTorch and TensorFlow Building and training deep neural networks Model saving/loading, GPU training

WEEK 4

Week 4: Computer Vision and Image Processing Using OpenCV, PIL, PyTorch/TensorFlow for image loading, preprocessing Transfer learning with CNNs (ResNet, MobileNet)

WEEK 5

Week 5: NLP and LLMs using HuggingFace Using BERT, RoBERTa for classification/generation Tokenization, fine-tuning, and inference

WEEK 6

Week 6: Prompt Engineering, LangChain & LLM APIs Working with LangChain, templates, and memory modules Prompt tuning best practices Using OpenAI, Gemini, and Ollama APIs for inference

WEEK 7

Week 7: Retrieval-Augmented Generation (RAG) & Vector DBs Concepts of semantic search & embedding spaces Use of FAISS, Pinecone, ChromaDB

WEEK 8

Week 8: Agents and Autonomous AI (Langchain, LangGraph) Concepts of tool use, planning, multi-step task agents Implementing basic agents using LangChain

WEEK 9

Week 9: Data Visualization & Dashboards Tools: PowerBI, Tableau, Streamlit, Plotly Dashboard design, storytelling with data

WEEK 10

Week 10: Time Series Forecasting Exploratory Analysis of Time Series Forecasting using Facebook Prophet and DeepAR Trend, seasonality, holidays, anomaly detection

WEEK 11

Week 11: Big Data and Cloud Platforms - Google Cloud Platform Build and run an end-to-end pipeline in GCP Working with GCP, BigQuery, Vertex AI Concepts of Spark, Hadoop, Airflow, and model deployment

WEEK 12

Week 12: Model Explainability, Ethics & Mini-project Tools: SHAP, LIME for understanding predictions Concepts: data privacy, fairness, AI ethics Mini-project

Data Science and AI Lab

Course Structure & Syllabus